10-import

library(readxl)
library(readr)
library(tidyverse)

## ── Attaching packages ──────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.2.1     ✔ purrr   0.3.2
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ ggplot2 3.2.1     ✔ forcats 0.4.0

## ── Conflicts ─────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(dplyr)
library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

## The following object is masked from 'package:purrr':
## 
##     transpose

library(ggrepel)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

SVAC_data <- read_excel("SVAC_dataset update 2016 June 21.xlsx")

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W1298 / R1298C23: got 'Tibetan'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W1409 / R1409C23: got 'Rwanda; Burundi'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W1410 / R1410C23: got 'Rwanda'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX1421 / R1421C50: got 'teacher'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W1422 / R1422C23: got 'Rwanda'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X1438 / R1438C24: got 'Catholic'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X1491 / R1491C24: got 'Catholic'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX1492 / R1492C50: got 'neighbor'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2389 / R2389C50: got '"twnety-five year old
## Dinka women"'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2514 / R2514C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i
## = sheet, : Expecting numeric in AX2514 / R2514C50: got '“witnesses at
## market”'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2515 / R2515C25: got 'girls'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2521 / R2521C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2522 / R2522C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2523 / R2523C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2524 / R2524C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2525 / R2525C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2526 / R2526C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2527 / R2527C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2528 / R2528C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2529 / R2529C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2601 / R2601C25: got '“young women”'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2683 / R2683C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2683 / R2683C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2683 / R2683C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2683 / R2683C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2684 / R2684C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2684 / R2684C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2684 / R2684C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2684 / R2684C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2685 / R2685C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2685 / R2685C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2685 / R2685C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2685 / R2685C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2686 / R2686C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2686 / R2686C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2686 / R2686C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2686 / R2686C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2687 / R2687C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2687 / R2687C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2687 / R2687C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2687 / R2687C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2688 / R2688C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2688 / R2688C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2688 / R2688C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2688 / R2688C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2689 / R2689C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2689 / R2689C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2689 / R2689C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2689 / R2689C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2690 / R2690C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2690 / R2690C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2690 / R2690C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2690 / R2690C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2691 / R2691C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2691 / R2691C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2691 / R2691C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2691 / R2691C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W2692 / R2692C23: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X2692 / R2692C24: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2692 / R2692C25: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2692 / R2692C50: got '.'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2778 / R2778C25: got 'Secondary school aged'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2779 / R2779C25: got 'Secondary school aged
## children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2802 / R2802C25: got 'School children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2807 / R2807C25: got 'School aged children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX2807 / R2807C50: got 'In front of man there
## to buy nuts'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2808 / R2808C25: got 'School aged children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2809 / R2809C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2810 / R2810C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2811 / R2811C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2812 / R2812C25: got 'Children; young girls'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2813 / R2813C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2814 / R2814C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2815 / R2815C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2816 / R2816C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y2826 / R2826C25: got 'children'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W3060 / R3060C23: got 'foreigners'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W3062 / R3062C23: got 'Congolese (DRC)'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W3063 / R3063C23: got 'Congolese (DRC)'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX3142 / R3142C50: got '“interrogator”'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX3996 / R3996C50: got '“fellow passengers”'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i
## = sheet, : Expecting numeric in AX4153 / R4153C50: got 'Two civilian
## brothers'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X4581 / R4581C24: got 'Islam'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W4936 / R4936C23: got 'Kuwaiti'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W4937 / R4937C23: got 'Kuwaiti'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX5101 / R5101C50: got 'group of dinner
## guests'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX5131 / R5131C50: got '"Other volunteers"'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX5250 / R5250C50: got 'passerby'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX5347 / R5347C50: got '"in front of …other
## civilians"'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX5350 / R5350C50: got 'In front of the whole
## village'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y5355 / R5355C25: got 'Under 17'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX5371 / R5371C50: got 'civilians'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X5701 / R5701C24: got 'Muslim'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X5715 / R5715C24: got 'muslim'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X5717 / R5717C24: got 'Muslim'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in Y5717 / R5717C25: got 'child brearing age'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX5717 / R5717C50: got 'other potential
## victims'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X5723 / R5723C24: got 'mus'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W5779 / R5779C23: got 'non serb; Croatian'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X5997 / R5997C24: got 'muslim'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX6286 / R6286C50: got 'Someone passing by
## (killed for protesting the rape)'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W6350 / R6350C23: got 'Ethiopian'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W6352 / R6352C23: got '“Ethiopian”'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in AX6503 / R6503C50: got 'refugees'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in W6540 / R6540C23: got 'Serb, Roma'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X6646 / R6646C24: got 'Muslim'

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting numeric in X6648 / R6648C24: got 'Muslim'

hfi <- read_csv("hfi_cc_2018.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   ISO_code = col_character(),
##   countries = col_character(),
##   region = col_character()
## )

## See spec(...) for full column specifications.

country <- read_csv("country.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   X1 = col_character(),
##   `Personal Freedom` = col_double(),
##   `Economic Freedom` = col_double()
## )

hfi_women <- hfi %>% select(year, X1 = countries, region, contains("women")) %>% filter(year == 2016)

hfiwc <- full_join(hfi_women, country)

## Joining, by = "X1"

str(hfi)

## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 1458 obs. of  124 variables:
##  $ X1                                : num  5 6 7 8 9 10 11 12 13 14 ...
##  $ year                              : num  2016 2016 2016 2016 2016 ...
##  $ ISO_code                          : chr  "ALB" "DZA" "AGO" "ARG" ...
##  $ countries                         : chr  "Albania" "Algeria" "Angola" "Argentina" ...
##  $ region                            : chr  "Eastern Europe" "Middle East & North Africa" "Sub-Saharan Africa" "Latin America & the Caribbean" ...
##  $ pf_rol_procedural                 : num  6.66 NA NA 7.1 NA ...
##  $ pf_rol_civil                      : num  4.55 NA NA 5.79 NA ...
##  $ pf_rol_criminal                   : num  4.67 NA NA 4.34 NA ...
##  $ pf_rol                            : num  5.29 3.82 3.45 5.74 5 ...
##  $ pf_ss_homicide                    : num  8.92 9.46 8.06 7.62 8.81 ...
##  $ pf_ss_disappearances_disap        : num  10 10 5 10 10 10 10 10 10 10 ...
##  $ pf_ss_disappearances_violent      : num  10 9.29 10 10 10 ...
##  $ pf_ss_disappearances_organized    : num  10 5 7.5 7.5 7.5 10 10 7.5 NA 2.5 ...
##  $ pf_ss_disappearances_fatalities   : num  10 9.93 10 10 9.32 ...
##  $ pf_ss_disappearances_injuries     : num  10 9.99 10 9.99 9.93 ...
##  $ pf_ss_disappearances              : num  10 8.84 8.5 9.5 9.35 ...
##  $ pf_ss_women_fgm                   : num  10 10 10 10 10 10 10 10 NA 10 ...
##  $ pf_ss_women_missing               : num  7.5 7.5 10 10 5 10 10 7.5 NA 7.5 ...
##  $ pf_ss_women_inheritance_widows    : num  5 0 5 10 10 10 10 5 NA 0 ...
##  $ pf_ss_women_inheritance_daughters : num  5 0 5 10 10 10 10 10 NA 0 ...
##  $ pf_ss_women_inheritance           : num  5 0 5 10 10 10 10 7.5 NA 0 ...
##  $ pf_ss_women                       : num  7.5 5.83 8.33 10 8.33 ...
##  $ pf_ss                             : num  8.81 8.04 8.3 9.04 8.83 ...
##  $ pf_movement_domestic              : num  5 5 0 10 5 10 10 5 10 10 ...
##  $ pf_movement_foreign               : num  10 5 5 10 5 10 10 5 10 5 ...
##  $ pf_movement_women                 : num  5 5 10 10 10 10 10 5 NA 5 ...
##  $ pf_movement                       : num  6.67 5 5 10 6.67 ...
##  $ pf_religion_estop_establish       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_religion_estop_operate         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_religion_estop                 : num  10 5 10 7.5 5 10 10 2.5 NA 10 ...
##  $ pf_religion_harassment            : num  9.57 6.87 8.9 9.04 8.58 ...
##  $ pf_religion_restrictions          : num  8.01 2.96 7.46 6.85 5.09 ...
##  $ pf_religion                       : num  9.19 4.94 8.79 7.8 6.22 ...
##  $ pf_association_association        : num  10 5 2.5 7.5 7.5 10 10 2.5 NA 5 ...
##  $ pf_association_assembly           : num  10 5 2.5 10 7.5 10 10 5 NA 0 ...
##  $ pf_association_political_establish: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_association_political_operate  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_association_political          : num  10 5 2.5 5 5 10 10 2.5 NA 0 ...
##  $ pf_association_prof_establish     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_association_prof_operate       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_association_prof               : num  10 5 5 7.5 5 10 10 2.5 NA 10 ...
##  $ pf_association_sport_establish    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_association_sport_operate      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pf_association_sport              : num  10 5 7.5 7.5 7.5 10 10 2.5 NA 10 ...
##  $ pf_association                    : num  10 5 4 7.5 6.5 10 10 3 NA 5 ...
##  $ pf_expression_killed              : num  10 10 10 10 10 10 10 10 10 10 ...
##  $ pf_expression_jailed              : num  10 10 10 10 10 ...
##  $ pf_expression_influence           : num  5 2.67 2.67 5.67 3.33 ...
##  $ pf_expression_control             : num  5.25 4 2.5 5.5 4.25 7.75 8 0.25 7.25 0.75 ...
##  $ pf_expression_cable               : num  10 10 7.5 10 7.5 10 10 10 NA 7.5 ...
##  $ pf_expression_newspapers          : num  10 7.5 5 10 7.5 10 10 0 NA 7.5 ...
##  $ pf_expression_internet            : num  10 7.5 7.5 10 7.5 10 10 7.5 NA 2.5 ...
##  $ pf_expression                     : num  8.61 7.38 6.45 8.74 7.15 ...
##  $ pf_identity_legal                 : num  0 NA 10 10 7 7 10 0 NA NA ...
##  $ pf_identity_parental_marriage     : num  10 0 10 10 10 10 10 10 10 0 ...
##  $ pf_identity_parental_divorce      : num  10 5 10 10 10 10 10 10 10 0 ...
##  $ pf_identity_parental              : num  10 2.5 10 10 10 10 10 10 10 0 ...
##  $ pf_identity_sex_male              : num  10 0 0 10 10 10 10 10 10 10 ...
##  $ pf_identity_sex_female            : num  10 0 0 10 10 10 10 10 10 10 ...
##  $ pf_identity_sex                   : num  10 0 0 10 10 10 10 10 10 10 ...
##  $ pf_identity_divorce               : num  5 0 10 10 5 10 10 5 NA 0 ...
##  $ pf_identity                       : num  6.25 0.833 7.5 10 8 ...
##  $ pf_score                          : num  7.6 5.28 6.11 8.1 6.91 ...
##  $ pf_rank                           : num  57 147 117 42 84 11 8 131 64 114 ...
##  $ ef_government_consumption         : num  8.23 2.15 7.6 5.34 7.26 ...
##  $ ef_government_transfers           : num  7.51 7.82 8.89 6.05 7.75 ...
##  $ ef_government_enterprises         : num  8 0 0 6 8 10 10 0 7 10 ...
##  $ ef_government_tax_income          : num  9 7 10 7 5 5 4 9 10 10 ...
##  $ ef_government_tax_payroll         : num  7 2 9 1 5 5 3 4 10 10 ...
##  $ ef_government_tax                 : num  8 4.5 9.5 4 5 5 3.5 6.5 10 10 ...
##  $ ef_government                     : num  7.94 3.62 6.5 5.35 7 ...
##  $ ef_legal_judicial                 : num  2.67 4.19 1.84 3.69 3.87 ...
##  $ ef_legal_courts                   : num  3.15 4.33 1.97 2.93 4.2 ...
##  $ ef_legal_protection               : num  4.51 4.69 2.51 4.26 5.66 ...
##  $ ef_legal_military                 : num  8.33 4.17 3.33 7.5 5.83 ...
##  $ ef_legal_integrity                : num  4.17 5 4.17 3.33 5 ...
##  $ ef_legal_enforcement              : num  4.39 4.51 2.3 3.63 5.2 ...
##  $ ef_legal_restrictions             : num  6.49 6.63 5.46 6.86 9.8 ...
##  $ ef_legal_police                   : num  6.93 6.14 3.02 3.39 5.71 ...
##  $ ef_legal_crime                    : num  6.22 6.74 4.29 4.13 7.01 ...
##  $ ef_legal_gender                   : num  0.949 0.821 0.846 0.769 1 ...
##  $ ef_legal                          : num  5.07 4.69 2.96 3.9 5.81 ...
##  $ ef_money_growth                   : num  8.99 6.96 9.39 5.23 9.08 ...
##  $ ef_money_sd                       : num  9.48 8.34 4.99 5.22 9.26 ...
##  $ ef_money_inflation                : num  9.74 8.72 3.05 2 9.75 ...
##  $ ef_money_currency                 : num  10 5 5 10 10 10 10 5 0 10 ...
##  $ ef_money                          : num  9.55 7.25 5.61 5.61 9.52 ...
##  $ ef_trade_tariffs_revenue          : num  9.63 8.48 8.99 6.06 8.87 ...
##  $ ef_trade_tariffs_mean             : num  9.24 6.22 7.72 7.26 8.76 9.5 8.96 8.2 3.36 9.06 ...
##  $ ef_trade_tariffs_sd               : num  8.02 5.92 4.25 5.94 8.02 ...
##  $ ef_trade_tariffs                  : num  8.96 6.87 6.99 6.42 8.55 ...
##  $ ef_trade_regulatory_nontariff     : num  5.57 4.96 3.13 4.47 5.92 ...
##  $ ef_trade_regulatory_compliance    : num  9.405 0 0.917 5.156 8.466 ...
##  $ ef_trade_regulatory               : num  7.49 2.48 2.02 4.81 7.19 ...
##  $ ef_trade_black                    : num  10 5.56 10 0 10 ...
##  $ ef_trade_movement_foreign         : num  6.31 3.66 2.95 5.36 5.11 ...
##  $ ef_trade_movement_capital         : num  4.615 0 3.077 0.769 5.385 ...
##  $ ef_trade_movement_visit           : num  8.297 1.106 0.111 7.965 10 ...
##  $ ef_trade_movement                 : num  6.41 1.59 2.04 4.7 6.83 ...
##   [list output truncated]
##  - attr(*, "spec")=
##   .. cols(
##   ..   X1 = col_double(),
##   ..   year = col_double(),
##   ..   ISO_code = col_character(),
##   ..   countries = col_character(),
##   ..   region = col_character(),
##   ..   pf_rol_procedural = col_double(),
##   ..   pf_rol_civil = col_double(),
##   ..   pf_rol_criminal = col_double(),
##   ..   pf_rol = col_double(),
##   ..   pf_ss_homicide = col_double(),
##   ..   pf_ss_disappearances_disap = col_double(),
##   ..   pf_ss_disappearances_violent = col_double(),
##   ..   pf_ss_disappearances_organized = col_double(),
##   ..   pf_ss_disappearances_fatalities = col_double(),
##   ..   pf_ss_disappearances_injuries = col_double(),
##   ..   pf_ss_disappearances = col_double(),
##   ..   pf_ss_women_fgm = col_double(),
##   ..   pf_ss_women_missing = col_double(),
##   ..   pf_ss_women_inheritance_widows = col_double(),
##   ..   pf_ss_women_inheritance_daughters = col_double(),
##   ..   pf_ss_women_inheritance = col_double(),
##   ..   pf_ss_women = col_double(),
##   ..   pf_ss = col_double(),
##   ..   pf_movement_domestic = col_double(),
##   ..   pf_movement_foreign = col_double(),
##   ..   pf_movement_women = col_double(),
##   ..   pf_movement = col_double(),
##   ..   pf_religion_estop_establish = col_double(),
##   ..   pf_religion_estop_operate = col_double(),
##   ..   pf_religion_estop = col_double(),
##   ..   pf_religion_harassment = col_double(),
##   ..   pf_religion_restrictions = col_double(),
##   ..   pf_religion = col_double(),
##   ..   pf_association_association = col_double(),
##   ..   pf_association_assembly = col_double(),
##   ..   pf_association_political_establish = col_double(),
##   ..   pf_association_political_operate = col_double(),
##   ..   pf_association_political = col_double(),
##   ..   pf_association_prof_establish = col_double(),
##   ..   pf_association_prof_operate = col_double(),
##   ..   pf_association_prof = col_double(),
##   ..   pf_association_sport_establish = col_double(),
##   ..   pf_association_sport_operate = col_double(),
##   ..   pf_association_sport = col_double(),
##   ..   pf_association = col_double(),
##   ..   pf_expression_killed = col_double(),
##   ..   pf_expression_jailed = col_double(),
##   ..   pf_expression_influence = col_double(),
##   ..   pf_expression_control = col_double(),
##   ..   pf_expression_cable = col_double(),
##   ..   pf_expression_newspapers = col_double(),
##   ..   pf_expression_internet = col_double(),
##   ..   pf_expression = col_double(),
##   ..   pf_identity_legal = col_double(),
##   ..   pf_identity_parental_marriage = col_double(),
##   ..   pf_identity_parental_divorce = col_double(),
##   ..   pf_identity_parental = col_double(),
##   ..   pf_identity_sex_male = col_double(),
##   ..   pf_identity_sex_female = col_double(),
##   ..   pf_identity_sex = col_double(),
##   ..   pf_identity_divorce = col_double(),
##   ..   pf_identity = col_double(),
##   ..   pf_score = col_double(),
##   ..   pf_rank = col_double(),
##   ..   ef_government_consumption = col_double(),
##   ..   ef_government_transfers = col_double(),
##   ..   ef_government_enterprises = col_double(),
##   ..   ef_government_tax_income = col_double(),
##   ..   ef_government_tax_payroll = col_double(),
##   ..   ef_government_tax = col_double(),
##   ..   ef_government = col_double(),
##   ..   ef_legal_judicial = col_double(),
##   ..   ef_legal_courts = col_double(),
##   ..   ef_legal_protection = col_double(),
##   ..   ef_legal_military = col_double(),
##   ..   ef_legal_integrity = col_double(),
##   ..   ef_legal_enforcement = col_double(),
##   ..   ef_legal_restrictions = col_double(),
##   ..   ef_legal_police = col_double(),
##   ..   ef_legal_crime = col_double(),
##   ..   ef_legal_gender = col_double(),
##   ..   ef_legal = col_double(),
##   ..   ef_money_growth = col_double(),
##   ..   ef_money_sd = col_double(),
##   ..   ef_money_inflation = col_double(),
##   ..   ef_money_currency = col_double(),
##   ..   ef_money = col_double(),
##   ..   ef_trade_tariffs_revenue = col_double(),
##   ..   ef_trade_tariffs_mean = col_double(),
##   ..   ef_trade_tariffs_sd = col_double(),
##   ..   ef_trade_tariffs = col_double(),
##   ..   ef_trade_regulatory_nontariff = col_double(),
##   ..   ef_trade_regulatory_compliance = col_double(),
##   ..   ef_trade_regulatory = col_double(),
##   ..   ef_trade_black = col_double(),
##   ..   ef_trade_movement_foreign = col_double(),
##   ..   ef_trade_movement_capital = col_double(),
##   ..   ef_trade_movement_visit = col_double(),
##   ..   ef_trade_movement = col_double(),
##   ..   ef_trade = col_double(),
##   ..   ef_regulation_credit_ownership = col_double(),
##   ..   ef_regulation_credit_private = col_double(),
##   ..   ef_regulation_credit_interest = col_double(),
##   ..   ef_regulation_credit = col_double(),
##   ..   ef_regulation_labor_minwage = col_double(),
##   ..   ef_regulation_labor_firing = col_double(),
##   ..   ef_regulation_labor_bargain = col_double(),
##   ..   ef_regulation_labor_hours = col_double(),
##   ..   ef_regulation_labor_dismissal = col_double(),
##   ..   ef_regulation_labor_conscription = col_double(),
##   ..   ef_regulation_labor = col_double(),
##   ..   ef_regulation_business_adm = col_double(),
##   ..   ef_regulation_business_bureaucracy = col_double(),
##   ..   ef_regulation_business_start = col_double(),
##   ..   ef_regulation_business_bribes = col_double(),
##   ..   ef_regulation_business_licensing = col_double(),
##   ..   ef_regulation_business_compliance = col_double(),
##   ..   ef_regulation_business = col_double(),
##   ..   ef_regulation = col_double(),
##   ..   ef_score = col_double(),
##   ..   ef_rank = col_double(),
##   ..   hf_score = col_double(),
##   ..   hf_rank = col_double(),
##   ..   hf_quartile = col_double()
##   .. )

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

## corrplot 0.84 loaded

corrplot(cor(hfiwc %>% 
               select(pf_ss_women_fgm:pf_movement_women)), 
         method="color",  
         sig.level = 0.01, insig = "blank",
         addCoef.col = "black", 
         tl.srt=45, 
         type="upper"
         )

score = hfi %>% filter(year == 2016) %>% select(ISO_code, countries, region, pf_score, pf_rank, ef_score, ef_rank, hf_score, hf_rank) %>%  na.omit()
p1 =ggplot(score, aes(x = ef_score, y = pf_score, color = region), group = countries)

pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
                   "Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
                   "India", "Italy", "China", "South Africa", "Spain",
                   "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                   "United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
                   "New Zealand", "Singapore")
mR2 <- summary(lm(pf_score ~ 1 + ef_score , data = score))$r.squared
mR2 <- paste0(format(mR2, digits = 2), "%")

p2 = p1 + geom_smooth(mapping = aes(linetype = "r2"),
              method = "lm",se = FALSE,
              color = "red") + # geom_point(size = 2, stroke = 1.25) +
  geom_point(aes(size = round(hf_score)), alpha = 0.6)+ labs(size = "Human Freedom Score") + 
  geom_text_repel(aes(label = countries) , color = "black", size = 2.5, data = filter(score, countries %in% pointsToLabel), force = 2 ) + #check_overlap = TRUE
  scale_x_continuous(name = "Economic Freedom Score, 2016 (10 = Most Free)", limits = c(2.5,9.5), breaks = 2.5:9.5) +
  scale_y_continuous(name = "Personal Freedom Score, 2016 (10 = Most Free)", limits = c(2.5,9.5), breaks = 2.5:9.5) +
  scale_color_brewer(name = "" , type = 'div', palette = 'Spectral') +
  scale_linetype(name = "",
                 breaks = "r2",
                 labels = list(bquote(R^2==.(mR2))),
                 guide = guide_legend(override.aes = list(linetype = 1, size = 2, color = "red"), order=2)) +
  ggtitle("Economic and Personal Freedom 2016") +
  theme_minimal() + # start with a minimal theme and add what we need
  theme(text = element_text(color = "gray20"),
        legend.text = element_text(size = 10, color = "gray10"),
        axis.text = element_text(face = "italic"),
        axis.title.x = element_text(vjust = -1), # move title away from axis
        axis.title.y = element_text(vjust = 2), # move away for axis
        axis.ticks.y = element_blank(), # element_blank() is how we remove elements
        axis.line = element_line(color = "gray40", size = 0.3),
        #panel.grid.major = element_blank()
        #axis.line.y = element_blank()
        panel.grid.major = element_line(color = "gray", size = 0.2)
        #panel.grid.major.x = element_blank()
        )
  

suppressWarnings(print(p2))

hfi_cc_2018 <- read_csv("hfi_cc_2018.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   ISO_code = col_character(),
##   countries = col_character(),
##   region = col_character()
## )

## See spec(...) for full column specifications.

#2008 Personal Freedom vs Economic Freedom
score = hfi_cc_2018 %>% filter(year == 2016) %>% select(ISO_code, countries, region, pf_score, pf_rank, ef_score, ef_rank, hf_score, hf_rank) %>%  na.omit()
p1 =ggplot(score, aes(x = ef_score, y = pf_score, color = region), group = countries)

pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
                   "Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
                   "India", "Italy", "China", "South Africa", "Spain",
                   "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                   "United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
                   "New Zealand", "Singapore")
mR2 <- summary(lm(pf_score ~ 1 + ef_score , data = score))$r.squared
mR2 <- paste0(format(mR2, digits = 2), "%")

p2 = p1 + geom_smooth(mapping = aes(linetype = "r2"),
              method = "lm",se = FALSE,
              color = "red") + # geom_point(size = 2, stroke = 1.25) +
  geom_point(aes(size = round(hf_score)), alpha = 0.6)+ labs(size = "Human Freedom Score") + 
  geom_text_repel(aes(label = countries) , color = "black", size = 2.5, data = filter(score, countries %in% pointsToLabel), force = 2 ) + #check_overlap = TRUE
  scale_x_continuous(name = "Economic Freedom Score, 2016 (10 = Most Free)", limits = c(2.5,9.5), breaks = 2.5:9.5) +
  scale_y_continuous(name = "Personal Freedom Score, 2016 (10 = Most Free)", limits = c(2.5,9.5), breaks = 2.5:9.5) +
  scale_color_brewer(name = "" , type = 'div', palette = 'Spectral') +
  scale_linetype(name = "",
                 breaks = "r2",
                 labels = list(bquote(R^2==.(mR2))),
                 guide = guide_legend(override.aes = list(linetype = 1, size = 2, color = "red"), order=2)) +
  ggtitle("Economic and Personal Freedom 2016") +
  theme_minimal() + # start with a minimal theme and add what we need
  theme(text = element_text(color = "gray20"),
        legend.text = element_text(size = 10, color = "gray10"),
        axis.text = element_text(face = "italic"),
        axis.title.x = element_text(vjust = -1), # move title away from axis
        axis.title.y = element_text(vjust = 2), # move away for axis
        axis.ticks.y = element_blank(), # element_blank() is how we remove elements
        axis.line = element_line(color = "gray40", size = 0.3),
        #panel.grid.major = element_blank()
        #axis.line.y = element_blank()
        panel.grid.major = element_line(color = "gray", size = 0.2)
        #panel.grid.major.x = element_blank()
        )
  

suppressWarnings(print(p2))

#chloropeth map of Human freedom score 2016 

# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
# specify map projection/options
g <- list(
showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'Mercator')
)

plot_geo(score) %>%
add_trace(
z = ~ round(hf_score, 2), color = ~hf_score, colors = 'Blues',
text = ~countries, locations = ~ISO_code, marker = list(line = l)
) %>%
colorbar(title = 'Human Freedom Score, 2016') %>%
layout(
title = 'Human Freedome Score, 2016',
geo = g
)

#2016 women Personal Freedom vs Economic Freedom
score = hfi_cc_2018 %>% filter(year == 2016) %>% select(ISO_code, countries, region, pf_score, pf_rank, ef_score, ef_rank, hf_score, hf_rank) %>%  na.omit()
p1 =ggplot(score, aes(x = ef_score, y = pf_score, color = region), group = countries)

pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
                   "Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
                   "India", "Italy", "China", "South Africa", "Spain",
                   "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                   "United States", "Germany", "Britain", "Barbados", "Norway", "Japan",
                   "New Zealand", "Singapore")
mR2 <- summary(lm(pf_score ~ 1 + ef_score , data = score))$r.squared
mR2 <- paste0(format(mR2, digits = 2), "%")

p2 = p1 + geom_smooth(mapping = aes(linetype = "r2"),
              method = "lm",se = FALSE,
              color = "red") + # geom_point(size = 2, stroke = 1.25) +
  geom_point(aes(size = round(hf_score)), alpha = 0.6)+ labs(size = "Human Freedom Score") + 
  geom_text_repel(aes(label = countries) , color = "black", size = 2.5, data = filter(score, countries %in% pointsToLabel), force = 2 ) + #check_overlap = TRUE
  scale_x_continuous(name = "Economic Freedom Score, 2016 (10 = Most Free)", limits = c(2.5,9.5), breaks = 2.5:9.5) +
  scale_y_continuous(name = "Personal Freedom Score, 2016 (10 = Most Free)", limits = c(2.5,9.5), breaks = 2.5:9.5) +
  scale_color_brewer(name = "" , type = 'div', palette = 'Spectral') +
  scale_linetype(name = "",
                 breaks = "r2",
                 labels = list(bquote(R^2==.(mR2))),
                 guide = guide_legend(override.aes = list(linetype = 1, size = 2, color = "red"), order=2)) +
  ggtitle("Economic and Personal Freedom 2016") +
  theme_minimal() + # start with a minimal theme and add what we need
  theme(text = element_text(color = "gray20"),
        legend.text = element_text(size = 10, color = "gray10"),
        axis.text = element_text(face = "italic"),
        axis.title.x = element_text(vjust = -1), # move title away from axis
        axis.title.y = element_text(vjust = 2), # move away for axis
        axis.ticks.y = element_blank(), # element_blank() is how we remove elements
        axis.line = element_line(color = "gray40", size = 0.3),
        #panel.grid.major = element_blank()
        #axis.line.y = element_blank()
        panel.grid.major = element_line(color = "gray", size = 0.2)
        #panel.grid.major.x = element_blank()
        )
  

suppressWarnings(print(p2))

#2008 personal freedom
df = hfi_cc_2018 %>% filter(year == 2008) %>% select(year, ISO_code, countries, region, starts_with('pf_'))

df=df[,colSums(is.na(df))<nrow(df)]

df = df %>% na.omit()
cormat <- round(cor(df[,5:57]),2)

#
get_lower_tri<-function(cormat){
  cormat[upper.tri(cormat)] <- NA
  return(cormat)
}
get_upper_tri <- function(cormat){
  cormat[lower.tri(cormat)]<- NA
  return(cormat)
}

#
reorder_cormat <- function(cormat){
  # Use correlation between variables as distance
  dd <- as.dist((1-cormat)/2) #what does this do?
  hc <- hclust(dd)
  cormat <-cormat[hc$order, hc$order]
}

# Reorder the correlation matrix
cormat <- reorder_cormat(cormat)
upper_tri <- get_upper_tri(cormat)
# Melt the correlation matrix
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Create a ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1,1), space = "Lab", 
                       name="Pearson\nCorrelation") +
  theme_minimal()+ # minimal theme
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 8.5, hjust = 1), 
        panel.grid.major = element_line(color = "gray", size = 0.2)) +
  coord_fixed()

ggheatmap + 
  #geom_text(aes(Var2, Var1, label = value), color = "black", size = 2.5) +
  theme(
    #axis.line = element_line(color = "gray40", size = 0.3),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major = element_line(color = "gray", size = 0.2),
    #panel.grid.major.x = element_blank(),
    panel.border = element_blank(),
    panel.background = element_blank(),
    axis.ticks = element_blank()
    ) + ggtitle("Personal Freedom, 2008")